feat(api): computed state-school benchmarks

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0146VHeLAWjDVE2B5uU67jCB
2026-07-13 18:45:30 +01:00
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+"""compute_benchmarks: state-school benchmarks computed from our dataset
+(spec §5/§8.6). The disadvantaged average must be weighted by cohort size,
+medians must ignore NaN, and only the latest year counts."""
+
+import numpy as np
+import pandas as pd
+
+from backend.data_loader import compute_benchmarks
+
+LATEST = 202425
+
+
+def _df():
+    rows = [
+        # Six primary schools, latest year. Disadvantaged RWM chosen so the
+        # weighted average differs clearly from the unweighted mean:
+        # weighted = (40*100 + 60*300) / 400 = 55.0 ; unweighted mean = 50.0
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=100,
+             rwm_expected_disadvantaged_pct=40.0, eal_pct=10.0,
+             sen_support_pct=10.0, disadvantaged_pct=20.0, total_pupils=200),
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=300,
+             rwm_expected_disadvantaged_pct=60.0, eal_pct=20.0,
+             sen_support_pct=14.0, disadvantaged_pct=24.0, total_pupils=280),
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=np.nan,
+             rwm_expected_disadvantaged_pct=99.0, eal_pct=30.0,
+             sen_support_pct=18.0, disadvantaged_pct=30.0, total_pupils=300),
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=50,
+             rwm_expected_disadvantaged_pct=np.nan, eal_pct=np.nan,
+             sen_support_pct=np.nan, disadvantaged_pct=np.nan, total_pupils=np.nan),
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=40,
+             rwm_expected_disadvantaged_pct=np.nan, eal_pct=40.0,
+             sen_support_pct=20.0, disadvantaged_pct=40.0, total_pupils=350),
+        dict(year=LATEST, attainment_8_score=np.nan, eligible_pupils=60,
+             rwm_expected_disadvantaged_pct=np.nan, eal_pct=50.0,
+             sen_support_pct=22.0, disadvantaged_pct=44.0, total_pupils=400),
+        # Two secondary schools (attainment_8 non-null)
+        dict(year=LATEST, attainment_8_score=45.0, eligible_pupils=180,
+             rwm_expected_disadvantaged_pct=np.nan, eal_pct=15.0,
+             sen_support_pct=12.0, disadvantaged_pct=22.0, total_pupils=1000),
+        dict(year=LATEST, attainment_8_score=50.0, eligible_pupils=200,
+             rwm_expected_disadvantaged_pct=np.nan, eal_pct=25.0,
+             sen_support_pct=16.0, disadvantaged_pct=26.0, total_pupils=1200),
+        # An older-year primary row that must NOT influence anything
+        dict(year=202324, attainment_8_score=np.nan, eligible_pupils=500,
+             rwm_expected_disadvantaged_pct=1.0, eal_pct=99.0,
+             sen_support_pct=99.0, disadvantaged_pct=99.0, total_pupils=9999),
+    ]
+    return pd.DataFrame(rows)
+
+
+def test_weighted_disadvantaged_average():
+    b = compute_benchmarks(_df())
+    # Row 3 has NaN eligible_pupils and must be excluded from the weighting.
+    assert b["primary"]["disadvantaged_rwm_expected_pct"] == 55.0
+
+
+def test_medians_ignore_nan_and_older_years():
+    b = compute_benchmarks(_df())
+    assert b["year"] == LATEST
+    # eal medians over [10,20,30,40,50] = 30
+    assert b["primary"]["eal_pct"] == 30.0
+    # median pupils over [200,280,300,350,400] = 300
+    assert b["primary"]["median_pupils"] == 300
+
+
+def test_secondary_block_has_no_disadvantaged_rwm():
+    b = compute_benchmarks(_df())
+    assert "disadvantaged_rwm_expected_pct" not in b["secondary"]
+    assert b["secondary"]["median_pupils"] == 1100
+
+
+def test_provenance_string():
+    b = compute_benchmarks(_df())
+    assert b["source"] == "state-school average (computed from our dataset)"
+
+
+def test_empty_df():
+    assert compute_benchmarks(pd.DataFrame()) == {}